Picture archiving and communications systems (PACS) are employed for acquiring images from various modalities, storing and retrieving the acquired images, and distributing and presenting the acquired images on a display workstation. Such modalities can include Magnetic Resonance Imaging (MRI), Computer Tomography (CT), Positron Emission Tomography (PET), and Ultrasonography (US), computed radiography (CR), digital radiography (DR), and the like.
In displaying an image on a PACS system, an image is preferred to be displayed in its correct orientation. This can be readily accomplished if the image is acquired from Magnetic Resonance Imaging (MRI), Computer Tomography (CT), Positron Emission Tomography (PET), and Ultrasonography (US), since the orientation of the image is already known during the acquisition.
However, for other modalities, for example Computed Radiography (CR), the image orientation is an issue since the orientation is not known, because CR cassettes, like film cassettes, are independent of the capture equipments. That is, they can be placed at various orientations to accommodate examination conditions. Therefore, the image orientation is unknown until it is processed and displayed on screen.
This situation also exists in digitized films. When a radiology department invests in digital image retrieval system, it is often necessary to digitize a large number of films to provide on-line recall of previous examinations in the PACS. The large-scale digitization of radiographic film is often performed using bulk feeders; therefore, the orientation of the film as it is digitized is often unknown to the PACS.
Currently, the operation of correcting image orientation is usually performed manually by technologists. Although rotating an image on a workstation can be accomplished in a few moments, when given a large number of radiographs to review, the accumulated time spent and cost required can be substantial.
Moreover, there are other factors associated with the need of correcting image orientation. For example, proper orientation of an image could benefit image quality and improve diagnostic confidence. Accurate medical diagnosis often depends on the correct display of diagnostically relevant regions in images. Correctly oriented images can promote the consistence of position of diagnostically relevant regions in radiographs, facilitate the segmentation and extraction of these regions, and further help to design more robust and effective image processing algorithms for rendering and displaying images.
Accordingly, a need exists for a method for automatically detecting the orientation of radiographs and reorienting them (if necessary) to a position preferred by radiologist (or other viewer). Such a method would promote the efficiency and effectiveness of image management and display in PACS, and expedite workflow in hospitals.
However, recognizing orientation of radiographs is a challenging problem as radiographs are often taken under a variety of examination conditions. The patient's pose and size can be a variant; the radiologist may have a preference depending on the patient's situation. These factors can result in radiographs from the same examination that appear quite different. Humans tend to use high level concepts to identify the correct orientation of an image by capturing the image contents, grouping them into meaningful objects and matching them with contextual information. However all these analysis procedures are difficult for computer to achieve in the real world.
Some approaches have been proposed to identify the orientation of chest radiographs.
For example, Pieka et al. (“Orientation Correction for Chest Images”, Journal of Digital Imaging, Vol. 5, No. 3, 1992) presented an automatic method to determine the projection and orientation of chest images using two projection profiles of images, obtained by calculating the average densities along horizontal and vertical lines.
Boone et. al. (“Recognition of Chest Radiograph Orientation for Picture Archiving and Communication Systems Display Using Neural Networks”, Journal of Digital Imaging, Vol. 5, No. 3, 1992) presented an artificial neural network to classify the orientation of chest radiographs. The features extracted include two projection profiles and four regions of interest.
Evanoff et. Al. (“Automatically Determining the Orientation of Chest Images”, SPIE Vol. 3035) applied linear regression on two orthogonal profiles to determine the top of the image, then sought the edge of heart to determine if the image requires reorientation.
Due to the characteristics of chest radiographs, the approaches mentioned above may have achieved certain degrees of success in their particular applications. However, these approaches are not appropriate for other exam type radiographs, such as elbow, knee, cervical spine and the like, because the projection profile is an accumulated pixel value along rows and columns, and is not sufficiently detailed to provide the image orientation information. Moreover it is very sensitive to the noise and patient's position.
Natural scene image orientation detection has been considered. Vailaya et. al. (“Automatic image orientation detection”, IEEE International Conference on Image Processing, Vol. 2, 1999) employed Bayesian learning network to classify the image orientation. Wang et al. (“Detecting image orientation based on low-level visual content”, Computer Vision and Image Understanding, Vol. 93, 2004) presented an automated image orientation detection algorithm based on both structural and color (low level) content features. In both methods, a feature is color. However, since the color-based features are not available in radiographs, the methods are not suitable for radiograph orientation detection.
Document image orientation detection is known. However, because of the significant difference between document images and radiographs, these methods are not suited for the orientation of radiographs.
Accordingly, there exists a need for a method to automatically detect the orientation of radiographs. Such a method should be robust and suited to accommodate large variations in radiographs